Abstract [en]

Random indexing (RI) is a lightweight dimension reduction method, which is used for example to approximate vector-semantic relationships in online natural language processing systems. Here we generalise RI to multi-dimensional arrays and thereby enable approximation of higher-order statistical relationships in data. The generalised method is a sparse implementation of random projections,which is the theoretical basis also for ordinary RI and other randomisation approaches to dimensionality reduction and data representation. We present numerical experiments which demonstrate that a multi-dimensional generalisation of RI is feasible, including comparisons with ordinary RI and principal component analysis (PCA). The RI method is well suited for online processing of data streams because relationship weights can be updated incrementally in a fixed-size distributed representation,and inner products can be approximated on the fly at low computational cost. An open source implementation of generalised RI is provided.